| dc.rights.license | CC-BY-NC-ND | |
| dc.contributor.advisor | Grelli, A.G. | |
| dc.contributor.author | Bos, Steven | |
| dc.date.accessioned | 2023-08-10T00:03:26Z | |
| dc.date.available | 2023-08-10T00:03:26Z | |
| dc.date.issued | 2023 | |
| dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/44580 | |
| dc.description.abstract | The newly proposed heavy-ion detector at CERN Large Hadron Collider, ALICE3, will face a hundredfold increase
in data rate due to the increased multiplicity and luminosity for both the proton-proton(pp) and lead-lead (Pb-
Pb) collisions. Current CPU and GPU hardware combinations account for only 3.5 Gb/s, more than an order of
magnitude lower than future demands. This research explores the use of machine learning algorithms on custom
Field-Programmable Gate Arrays (FPGAs), a combination not yet | |
| dc.description.sponsorship | Utrecht University | |
| dc.language.iso | EN | |
| dc.subject | The newly proposed heavy-ion detector at CERN Large Hadron Collider, ALICE3, will face a hundredfold increase
in data rate due to the increased multiplicity and luminosity for both the proton-proton(pp) and lead-lead (Pb-
Pb) collisions. Current CPU and GPU hardware combinations account for only 3.5 Gb/s, more than an order of
magnitude lower than future demands. This research explores the use of machine learning algorithms on custom
Field-Programmable Gate Arrays (FPGAs), a combination not yet | |
| dc.title | Accelerating Trigger Performance of the
ALICE Detector Using FPGA-Based
Neural Networks | |
| dc.type.content | Master Thesis | |
| dc.rights.accessrights | Open Access | |
| dc.subject.keywords | FPGA; Machine learning; Particle physics; ALICE | |
| dc.subject.courseuu | Experimental Physics | |
| dc.thesis.id | 21354 | |
